Abstract
Data protection in Germany has a long tradition (https://www.goethe.de/en/kul/med/20446236.html). For a long time, the German Federal Data Protection Act or Bundesdatenschutzgesetz (BDSG) was considered as one of the strictest. Since May 2017 the EU General Data Protection Regulation (GDPR) regulates data protection all over Europe and it strongly influenced by the German law. When recording data in public areas, the recordings may contain personal data, such as license plates or persons. According to the GDPR this processing of personal data has to fulfill certain requirements to be considered lawful. In this paper, we address recording visual data in public while abiding by the applicable laws. Towards this end, a formal data protection concept is developed for a mobile sensor platform. The core part of this data protection concept is the anonymization of personal data, which is implemented with state-of-the-art deep learning based methods achieving almost human-level performance. The methods are evaluated quantitatively and qualitatively on example data recorded with a real mobile sensor platform in an urban environment.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
References
Agrawal, P., Narayanan, P.: Person de-identification in videos. IEEE Trans. Circuits Syst. Video Technol. 21(3), 299–310 (2011)
Arsenovic, M., Sladojevic, S., Anderla, A., Stefanovic, D.: Deep learning driven plates recognition system. In: 17th International Scientific Conference Industrial Systems (IS 2017) (2017)
Bayerisches Landesamt für Datenschutzaufsicht: Bundesdatenschutzgesetz (BDSG); Rechtsgutachten zum möglichen Erprobungseinsatz eines mit Laser- und Kameratechnik ausgestatteten Sensorfahrzeuges im Stadtgebiet von Ettlingen, December 2015
Borgmann, B., Schatz, V., Kieritz, H., Scherer-Klöckling, C., Hebel, M., Arens, M.: Data processing and recording using a versatile multi-sensor vehicle. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. IV-1, 21–28 (2018)
Cao, Z., Simon, T., Wei, S.E., Sheikh, Y.: Realtime multi-person 2D pose estimation using part affinity fields. In: CVPR (2017)
Flores, A., Belongie, S.: Removing pedestrians from google street view images. In: CVPR - Workshops, June 2010
Frome, A., et al.: Large-scale privacy protection in Google street view. In: ICCV. IEEE (2009)
Grosselfinger, A.K., Münch, D., Arens, M.: An architecture for automatic multi-modal video data anonymization to ensure data protection. SPIE Security + Defence (2019)
HÄRTING Rechtsanwälte: RECHTSGUTACHTEN zu Rechtsfragen zum möglichen Erprobungseinsatz eines mit Laser- und Kameratechnik ausgestatteten Sensorfahrzeuges im Stadtgebiet Ettlingen durch das Fraunhofer Institut für Optronik, Systemtechnik und Bildauswertung (IOSB), Abteilung Objekterkennung, June 2013
Jørgensen, H.: Automatic license plate recognition using deep learning techniques. Master’s thesis, NTNU (2017)
Laroca, R., et al.: A robust real-time automatic license plate recognition based on the YOLO detector. arXiv preprint arXiv:1802.09567 (2018)
Li, H., Shen, C.: Reading car license plates using deep convolutional neural networks and LSTMs. arXiv preprint arXiv:1601.05610 (2016)
Masood, S.Z., Shu, G., Dehghan, A., Ortiz, E.G.: license plate detection and recognition using deeply learned convolutional neural networks. arXiv preprint arXiv:1703.07330 (2017)
McPherson, R., Shokri, R., Shmatikov, V.: Defeating image obfuscation with deep learning. arXiv preprint arXiv:1609.00408 (2016)
Nodari, A., Vanetti, M., Gallo, I.: Digital privacy: Replacing pedestrians from Google street view images. In: ICPR, November 2012
Padilla-López, J.R., Chaaraoui, A.A., Flórez-Revuelta, F.: Visual privacy protection methods: a survey. Expert Syst. Appl. 42(9), 4177–4195 (2015)
Peter, R., Grosselfinger, A.K., Münch, D., Arens, M.: Automated license plate detection for image anonymization. SPIE Security + Defence (2019)
Redmon, J., Farhadi, A.: Yolov3 an incremental improvement. arXiv preprint arXiv:1804.02767 (2018)
Ribaric, S., Ariyaeeinia, A., Pavesic, N.: De-identification for privacy protection in multimedia content: A survey. Sig. Process. Image Commun. 47, 131–151 (2016)
Uittenbogaard, R., Sebastian, C., Vijverberg, J., Boom, B., Gavrila, D.M., et al.: Privacy protection in street-view panoramas using depth and multi-view imagery. In: CVPR (2019)
Wei, S.E., Ramakrishna, V., Kanade, T., Sheikh, Y.: Convolutional pose machines. In: CVPR (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Münch, D., Grosselfinger, AK., Krempel, E., Hebel, M., Arens, M. (2019). Data Anonymization for Data Protection on Publicly Recorded Data. In: Tzovaras, D., Giakoumis, D., Vincze, M., Argyros, A. (eds) Computer Vision Systems. ICVS 2019. Lecture Notes in Computer Science(), vol 11754. Springer, Cham. https://doi.org/10.1007/978-3-030-34995-0_23
Download citation
DOI: https://doi.org/10.1007/978-3-030-34995-0_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-34994-3
Online ISBN: 978-3-030-34995-0
eBook Packages: Computer ScienceComputer Science (R0)